Instructions to use MawaredHR/Mawared_T1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MawaredHR/Mawared_T1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MawaredHR/Mawared_T1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MawaredHR/Mawared_T1")
model = AutoModelForCausalLM.from_pretrained("MawaredHR/Mawared_T1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MawaredHR/Mawared_T1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MawaredHR/Mawared_T1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MawaredHR/Mawared_T1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MawaredHR/Mawared_T1

SGLang

How to use MawaredHR/Mawared_T1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MawaredHR/Mawared_T1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MawaredHR/Mawared_T1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MawaredHR/Mawared_T1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MawaredHR/Mawared_T1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use MawaredHR/Mawared_T1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MawaredHR/Mawared_T1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MawaredHR/Mawared_T1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MawaredHR/Mawared_T1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="MawaredHR/Mawared_T1",
    max_seq_length=2048,
)

Docker Model Runner
How to use MawaredHR/Mawared_T1 with Docker Model Runner:
```
docker model run hf.co/MawaredHR/Mawared_T1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Bilingual Assistant Model Card

Overview

This bilingual language model is designed to support seamless text generation and understanding in both Arabic (ar) and English (en). Fine-tuned from the arcee-ai/Meraj-Mini base model, it offers robust multilingual capabilities optimized for various applications such as conversational agents, content creation, and multilingual text analysis.

Key Highlights

Multilingual Proficiency: Designed to handle complex linguistic nuances in both Arabic and English, ensuring high-quality outputs in both languages.
Performance Optimization: Achieved 2x faster training through innovative methods provided by the Unsloth framework and the Hugging Face TRL library.
Transformer-Based Architecture: Utilizes advanced transformer layers to deliver state-of-the-art performance in text generation and inference.

Development Details

Developer: Daemontatox
License: Licensed under the Apache-2.0, ensuring open accessibility and flexibility for various use cases.
Base Model: The model is a fine-tuned variant of arcee-ai/Meraj-Mini.
Frameworks Used:
- Unsloth: Enabled faster and more efficient training.
- Hugging Face TRL Library: Provided tools for reinforcement learning fine-tuning, enhancing model responsiveness and accuracy.

Training Process

The fine-tuning process was conducted with a focus on:

Data Diversity: Leveraged a bilingual corpus to ensure comprehensive language understanding across both supported languages.
Optimized Hardware Utilization: Implemented Unsloth's accelerated training methods, significantly reducing resource consumption and training time.
Reinforcement Learning: Used Hugging Face's TRL library to fine-tune the model's decision-making and response generation capabilities, particularly for conversational and contextual understanding.

Applications

This model is suited for a variety of real-world applications, including:

Conversational Agents: Powering bilingual chatbots and virtual assistants for customer support and personal use.
Content Generation: Assisting in drafting multilingual articles, social media posts, and creative writing.
Translation Support: Providing context-aware translations and summaries across Arabic and English.
Education: Enhancing learning platforms by offering bilingual educational content and interactive learning experiences.

Future Directions

Plans for extending the model's capabilities include:

Additional Language Support: Exploring fine-tuning for additional languages.
Domain-Specific Training: Specializing the model for industries such as healthcare, legal, and technical writing.
Optimization for Edge Devices: Investigating quantization techniques to deploy the model on resource-constrained hardware like mobile devices and IoT platforms.